Usually, the quantitative evaluation of a segmentation algorithm is done by comparing the automatic segmentation with one
or more reference segmentations of the ground-truth corpus. In the literature one can find several metrics to evaluate
quantitatively the similarity between two segmentations of a triangulated surface [1]}, [2]}.
In this section we employ two different non-parametric measures: the Jaccard index \(JI\)  [3]} and the Rand index
\(RI\)  [4]}. The segmentation of a mesh with \(n\)  triangles may be described by a vector \(s=(s_1,...,s_n)\) ,
where \(s_j\)  is the index of the cluster to which the \(j\) -th triangle belongs. Given two segmentations \(s_a\)  and \(s_b\) 
of the same mesh, we denote by \(JI(s_a,s_b)\)  and \(RI(s_a,s_b)\)  the similarity between them according to the Jaccard
and Rand indexes respectively. For both indexes it holds: \(0 \le JI(s_a,s_b) \le 1\)  and \(0 \le RI(s_a,s_b) \le 1\) ,
where the value 1 corresponds to the maximal similarity, i.e. \(JI(s_a,s_b)=1\)  or \(RI(s_a,s_b)=1\)  means that segmentations
\(s_a\)  and \(s_b\)  are identical. In the experiments we compute Jaccard and Rand distances between \(s_a\)  and \(s_b\)  given
by, \(d_J(s_a,s_b):=1-JI(s_a,s_b)\)  and \(d_R(s_a,s_b):=1-RI(s_a,s_b)\) .
